Identification of herbarium specimen sheet components from high-resolution images using deep learning.

Advanced computer vision techniques hold the potential to mobilise vast quantities of biodiversity data by facilitating the rapid extraction of text- and trait-based data from herbarium specimen digital images, and to increase the efficiency and accuracy of downstream data capture during digitisation. This investigation developed an object detection model using YOLOv5 and digitised collection images from the University of Melbourne Herbarium (MELU). The MELU-trained 'sheet-component' model-trained on 3371 annotated images, validated on 1000 annotated images, run using 'large' model type, at 640 pixels, for 200 epochs-successfully identified most of the 11 component types of the digital specimen images, with an overall model precision measure of 0.983, recall of 0.969 and moving average precision (mAP0.5-0.95) of 0.847. Specifically, 'institutional' and 'annotation' labels were predicted with mAP0.5-0.95 of 0.970 and 0.878 respectively. It was found that annotating at least 2000 images was required to train an adequate model, likely due to the heterogeneity of specimen sheets. The full model was then applied to selected specimens from nine global herbaria (Biodiversity Data Journal, 7, 2019), quantifying its generalisability: for example, the 'institutional label' was identified with mAP0.5-0.95 of between 0.68 and 0.89 across the various herbaria. Further detailed study demonstrated that starting with the MELU-model weights and retraining for as few as 50 epochs on 30 additional annotated images was sufficient to enable the prediction of a previously unseen component. As many herbaria are resource-constrained, the MELU-trained 'sheet-component' model weights are made available and application encouraged.

The influence of foraging benefits on association of cattle egrets (Bubulcus ibis) with cattle.

The degree of association between cattle egrets (Bubulcus ibis) and cattle was studied during one summer on Saint Catherines Island, Georgia, USA. Previous work by Grubb (1976) and others indicated that cattle egrets foraging with cattle require fewer steps and less time to catch prey than egrets foraging without cattle and single egrets catch prey at a higher rate than egrets foraging in groups of two or more with cattle. Accordingly, we predicted that when given a choice egrets should forage with cattle rather than alone, egrets should prefer to associate with standing rather than sitting cattle, and single egrets associated with cattle should be more common than expected by chance.In excess of two-thirds of the egrets accompanied cattle. Neither time of day nor month influenced the degree of association, but egrets in forest were more likely to be associated with cattle than egrets in pasture. Standing cattle were more likely to be accompanied by egrets than were sitting cattle. Single egrets occurred more frequently than expected by chance when accompanying standing cattle but not when associated with sitting cattle. Thus, cattle egrets usually distributed themselves among cattle in the way predicted by optimal foraging theory.